Overview

Dataset statistics

Number of variables14
Number of observations582
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.8 KiB
Average record size in memory112.2 B

Variable types

Categorical4
Numeric10

Alerts

category has constant value "성인종합영양제" Constant
TV is highly correlated with cable and 2 other fieldsHigh correlation
cable is highly correlated with TV and 2 other fieldsHigh correlation
jong is highly correlated with TV and 2 other fieldsHigh correlation
sum is highly correlated with TV and 2 other fieldsHigh correlation
TV is highly correlated with cable and 2 other fieldsHigh correlation
cable is highly correlated with TV and 3 other fieldsHigh correlation
jong is highly correlated with TV and 3 other fieldsHigh correlation
UCC is highly correlated with cable and 2 other fieldsHigh correlation
sum is highly correlated with TV and 3 other fieldsHigh correlation
TV is highly correlated with cable and 2 other fieldsHigh correlation
cable is highly correlated with TV and 2 other fieldsHigh correlation
jong is highly correlated with TV and 2 other fieldsHigh correlation
sum is highly correlated with TV and 2 other fieldsHigh correlation
item is highly correlated with categoryHigh correlation
product is highly correlated with advertiser and 1 other fieldsHigh correlation
advertiser is highly correlated with product and 1 other fieldsHigh correlation
category is highly correlated with item and 2 other fieldsHigh correlation
advertiser is highly correlated with product and 1 other fieldsHigh correlation
product is highly correlated with advertiser and 2 other fieldsHigh correlation
date is highly correlated with productHigh correlation
item is highly correlated with cable and 3 other fieldsHigh correlation
TV is highly correlated with cable and 4 other fieldsHigh correlation
radio is highly correlated with advertiser and 1 other fieldsHigh correlation
newspaper is highly correlated with magazineHigh correlation
magazine is highly correlated with newspaper and 1 other fieldsHigh correlation
cable is highly correlated with item and 4 other fieldsHigh correlation
jong is highly correlated with item and 4 other fieldsHigh correlation
UCC is highly correlated with item and 4 other fieldsHigh correlation
banner is highly correlated with TV and 2 other fieldsHigh correlation
sum is highly correlated with item and 5 other fieldsHigh correlation
banner is highly skewed (γ1 = 23.71926611) Skewed
TV has 342 (58.8%) zeros Zeros
radio has 540 (92.8%) zeros Zeros
newspaper has 387 (66.5%) zeros Zeros
magazine has 432 (74.2%) zeros Zeros
cable has 252 (43.3%) zeros Zeros
jong has 282 (48.5%) zeros Zeros
UCC has 354 (60.8%) zeros Zeros
banner has 569 (97.8%) zeros Zeros

Reproduction

Analysis started2022-05-02 04:37:23.853301
Analysis finished2022-05-02 04:37:34.089491
Duration10.24 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

category
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
성인종합영양제
582 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성인종합영양제
2nd row성인종합영양제
3rd row성인종합영양제
4th row성인종합영양제
5th row성인종합영양제

Common Values

ValueCountFrequency (%)
성인종합영양제582
100.0%

Length

2022-05-02T13:37:34.147490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-02T13:37:34.197489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
성인종합영양제582
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

advertiser
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
GC녹십자
208 
일동제약
168 
글락소스미스클라인컨슈머헬스케어코리아
94 
한국화이자제약(주)
58 
구주제약
21 
Other values (2)
33 

Length

Max length19
Median length5
Mean length7.378006873
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구주제약
2nd row구주제약
3rd row구주제약
4th row구주제약
5th row구주제약

Common Values

ValueCountFrequency (%)
GC녹십자208
35.7%
일동제약168
28.9%
글락소스미스클라인컨슈머헬스케어코리아94
16.2%
한국화이자제약(주)58
 
10.0%
구주제약21
 
3.6%
삼진제약21
 
3.6%
유유제약12
 
2.1%

Length

2022-05-02T13:37:34.254492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-02T13:37:34.310489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gc녹십자208
35.7%
일동제약168
28.9%
글락소스미스클라인컨슈머헬스케어코리아94
16.2%
한국화이자제약(주58
 
10.0%
구주제약21
 
3.6%
삼진제약21
 
3.6%
유유제약12
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

product
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
아로나민골드
100 
GC녹십자비맥스메타정
75 
글락소스미스클라인센트룸
69 
아로나민씨플러스
62 
GC녹십자비맥스골드
60 
Other values (15)
216 

Length

Max length20
Median length10
Mean length9.651202749
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row구주알코덱스
2nd row구주알코덱스
3rd row구주알코덱스
4th row구주알코덱스
5th row구주알코덱스

Common Values

ValueCountFrequency (%)
아로나민골드100
17.2%
GC녹십자비맥스메타정75
12.9%
글락소스미스클라인센트룸69
11.9%
아로나민씨플러스62
10.7%
GC녹십자비맥스골드60
10.3%
한국화이자센트룸포맨&포우먼39
 
6.7%
GC녹십자비맥스액티브정36
 
6.2%
GC녹십자비맥스36
 
6.2%
구주알코덱스21
 
3.6%
삼진제약트레스탄21
 
3.6%
Other values (10)63
10.8%

Length

2022-05-02T13:37:34.411600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아로나민골드100
17.2%
gc녹십자비맥스메타정75
12.9%
글락소스미스클라인센트룸69
11.9%
아로나민씨플러스62
10.7%
gc녹십자비맥스골드60
10.3%
한국화이자센트룸포맨&포우먼39
 
6.7%
gc녹십자비맥스액티브정36
 
6.2%
gc녹십자비맥스36
 
6.2%
구주알코덱스21
 
3.6%
삼진제약트레스탄21
 
3.6%
Other values (10)63
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.05285223
Minimum19.01
Maximum22.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:34.504599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19.01
5-th percentile19.03
Q119.1
median20.06
Q321.02
95-th percentile21.1
Maximum22.02
Range3.01
Interquartile range (IQR)1.92

Descriptive statistics

Standard deviation0.8482766424
Coefficient of variation (CV)0.04230204425
Kurtosis-1.068898989
Mean20.05285223
Median Absolute Deviation (MAD)0.96
Skewness0.2530244151
Sum11670.76
Variance0.7195732621
MonotonicityNot monotonic
2022-05-02T13:37:34.595598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
20.0624
 
4.1%
20.0122
 
3.8%
21.0222
 
3.8%
19.0721
 
3.6%
19.0620
 
3.4%
19.1220
 
3.4%
19.0820
 
3.4%
20.119
 
3.3%
21.0819
 
3.3%
20.0518
 
3.1%
Other values (28)377
64.8%
ValueCountFrequency (%)
19.0114
2.4%
19.0211
1.9%
19.0315
2.6%
19.0415
2.6%
19.0514
2.4%
19.0620
3.4%
19.0721
3.6%
19.0820
3.4%
19.0915
2.6%
19.118
3.1%
ValueCountFrequency (%)
22.029
1.5%
22.016
 
1.0%
21.126
 
1.0%
21.116
 
1.0%
21.112
2.1%
21.0912
2.1%
21.0819
3.3%
21.0712
2.1%
21.0614
2.4%
21.0514
2.4%

item
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
횟수
202 
금액
201 
노출량
179 

Length

Max length3
Median length2
Mean length2.307560137
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금액
2nd row횟수
3rd row노출량
4th row금액
5th row횟수

Common Values

ValueCountFrequency (%)
횟수202
34.7%
금액201
34.5%
노출량179
30.8%

Length

2022-05-02T13:37:34.688599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-02T13:37:34.745600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
횟수202
34.7%
금액201
34.5%
노출량179
30.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct238
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92539.0378
Minimum0
Maximum3660470
Zeros342
Zeros (%)58.8%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:34.820599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32553.75
95-th percentile679057.05
Maximum3660470
Range3660470
Interquartile range (IQR)2553.75

Descriptive statistics

Standard deviation306616.3064
Coefficient of variation (CV)3.313372537
Kurtosis46.48892914
Mean92539.0378
Median Absolute Deviation (MAD)0
Skewness5.664565475
Sum53857720
Variance9.401355937 × 1010
MonotonicityNot monotonic
2022-05-02T13:37:34.922599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0342
58.8%
40202
 
0.3%
2032
 
0.3%
93902
 
0.3%
3971
 
0.2%
47851
 
0.2%
1550271
 
0.2%
1551
 
0.2%
23251
 
0.2%
8023121
 
0.2%
Other values (228)228
39.2%
ValueCountFrequency (%)
0342
58.8%
11
 
0.2%
51
 
0.2%
151
 
0.2%
231
 
0.2%
551
 
0.2%
561
 
0.2%
841
 
0.2%
1051
 
0.2%
1311
 
0.2%
ValueCountFrequency (%)
36604701
0.2%
28518391
0.2%
16228071
0.2%
15648141
0.2%
15567471
0.2%
14148271
0.2%
13723861
0.2%
11502731
0.2%
11097301
0.2%
10411341
0.2%

radio
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct43
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1907.805842
Minimum0
Maximum124405
Zeros540
Zeros (%)92.8%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:35.024598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile471.2
Maximum124405
Range124405
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13054.10718
Coefficient of variation (CV)6.842471542
Kurtosis59.15175943
Mean1907.805842
Median Absolute Deviation (MAD)0
Skewness7.676445452
Sum1110343
Variance170409714.3
MonotonicityNot monotonic
2022-05-02T13:37:35.124598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0540
92.8%
2561
 
0.2%
51001
 
0.2%
791681
 
0.2%
2841
 
0.2%
56801
 
0.2%
776431
 
0.2%
2521
 
0.2%
50401
 
0.2%
775761
 
0.2%
Other values (33)33
 
5.7%
ValueCountFrequency (%)
0540
92.8%
681
 
0.2%
941
 
0.2%
971
 
0.2%
1241
 
0.2%
2521
 
0.2%
2551
 
0.2%
2561
 
0.2%
2841
 
0.2%
3991
 
0.2%
ValueCountFrequency (%)
1244051
0.2%
1135451
0.2%
1117431
0.2%
1097981
0.2%
1081631
0.2%
1050831
0.2%
791681
0.2%
785161
0.2%
776431
0.2%
775761
0.2%

newspaper
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct129
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3897.43299
Minimum0
Maximum240745
Zeros387
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:35.221599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile17490.15
Maximum240745
Range240745
Interquartile range (IQR)14

Descriptive statistics

Standard deviation18974.27627
Coefficient of variation (CV)4.868403463
Kurtosis89.78276971
Mean3897.43299
Median Absolute Deviation (MAD)0
Skewness8.790451178
Sum2268306
Variance360023159.8
MonotonicityNot monotonic
2022-05-02T13:37:35.322600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0387
66.5%
67
 
1.2%
76
 
1.0%
16
 
1.0%
36
 
1.0%
25
 
0.9%
11345
 
0.9%
13234
 
0.7%
54
 
0.7%
9453
 
0.5%
Other values (119)149
 
25.6%
ValueCountFrequency (%)
0387
66.5%
16
 
1.0%
25
 
0.9%
36
 
1.0%
42
 
0.3%
54
 
0.7%
67
 
1.2%
76
 
1.0%
82
 
0.3%
92
 
0.3%
ValueCountFrequency (%)
2407451
0.2%
2057701
0.2%
1926701
0.2%
1668901
0.2%
849151
0.2%
744171
0.2%
695741
0.2%
599401
0.2%
582131
0.2%
537921
0.2%

magazine
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424.7250859
Minimum0
Maximum17000
Zeros432
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:35.412598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4000
Maximum17000
Range17000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1657.763595
Coefficient of variation (CV)3.903145
Kurtosis30.96986809
Mean424.7250859
Median Absolute Deviation (MAD)0
Skewness5.061401683
Sum247190
Variance2748180.138
MonotonicityNot monotonic
2022-05-02T13:37:35.493598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0432
74.2%
244
 
7.6%
138
 
6.5%
312
 
2.1%
200012
 
2.1%
400010
 
1.7%
60004
 
0.7%
54
 
0.7%
45003
 
0.5%
80002
 
0.3%
Other values (17)21
 
3.6%
ValueCountFrequency (%)
0432
74.2%
138
 
6.5%
244
 
7.6%
312
 
2.1%
42
 
0.3%
54
 
0.7%
18002
 
0.3%
200012
 
2.1%
25001
 
0.2%
35002
 
0.3%
ValueCountFrequency (%)
170001
0.2%
110001
0.2%
105001
0.2%
102001
0.2%
100001
0.2%
98001
0.2%
85001
0.2%
80002
0.3%
70001
0.2%
65002
0.3%

cable
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct327
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102401.3162
Minimum0
Maximum2114145
Zeros252
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:35.592598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1120.5
Q347722.5
95-th percentile676126.35
Maximum2114145
Range2114145
Interquartile range (IQR)47722.5

Descriptive statistics

Standard deviation246695.7151
Coefficient of variation (CV)2.409106879
Kurtosis13.5076444
Mean102401.3162
Median Absolute Deviation (MAD)1120.5
Skewness3.282722085
Sum59597566
Variance6.085877586 × 1010
MonotonicityNot monotonic
2022-05-02T13:37:35.816598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0252
43.3%
153
 
0.5%
12
 
0.3%
30572
 
0.3%
19191
 
0.2%
44551
 
0.2%
2971
 
0.2%
1142581
 
0.2%
82051
 
0.2%
5471
 
0.2%
Other values (317)317
54.5%
ValueCountFrequency (%)
0252
43.3%
12
 
0.3%
81
 
0.2%
153
 
0.5%
261
 
0.2%
291
 
0.2%
371
 
0.2%
861
 
0.2%
1001
 
0.2%
1201
 
0.2%
ValueCountFrequency (%)
21141451
0.2%
15141751
0.2%
14504741
0.2%
11548061
0.2%
11051331
0.2%
10735881
0.2%
10646781
0.2%
9537191
0.2%
9408811
0.2%
9191621
0.2%

jong
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct288
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67332.03436
Minimum0
Maximum882647
Zeros282
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:35.920599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median48
Q35846.25
95-th percentile506917.25
Maximum882647
Range882647
Interquartile range (IQR)5846.25

Descriptive statistics

Standard deviation169069.5964
Coefficient of variation (CV)2.510983041
Kurtosis5.969114602
Mean67332.03436
Median Absolute Deviation (MAD)48
Skewness2.618236492
Sum39187244
Variance2.858452843 × 1010
MonotonicityNot monotonic
2022-05-02T13:37:36.017598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0282
48.5%
2042
 
0.3%
2862
 
0.3%
22802
 
0.3%
35402
 
0.3%
54302
 
0.3%
3622
 
0.3%
5002
 
0.3%
4142
 
0.3%
30602
 
0.3%
Other values (278)282
48.5%
ValueCountFrequency (%)
0282
48.5%
12
 
0.3%
21
 
0.2%
152
 
0.3%
281
 
0.2%
301
 
0.2%
381
 
0.2%
441
 
0.2%
521
 
0.2%
531
 
0.2%
ValueCountFrequency (%)
8826471
0.2%
8519951
0.2%
8255151
0.2%
7407851
0.2%
7271501
0.2%
7244621
0.2%
7218391
0.2%
7045261
0.2%
6522591
0.2%
6515741
0.2%

UCC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct196
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10145.73368
Minimum0
Maximum200322
Zeros354
Zeros (%)60.8%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:36.120601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q379.75
95-th percentile68930.15
Maximum200322
Range200322
Interquartile range (IQR)79.75

Descriptive statistics

Standard deviation30172.57874
Coefficient of variation (CV)2.973917875
Kurtosis15.4026131
Mean10145.73368
Median Absolute Deviation (MAD)0
Skewness3.815975053
Sum5904817
Variance910384507.6
MonotonicityNot monotonic
2022-05-02T13:37:36.227600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0354
60.8%
337
 
1.2%
483
 
0.5%
493
 
0.5%
453
 
0.5%
33
 
0.5%
322
 
0.3%
422
 
0.3%
42
 
0.3%
882
 
0.3%
Other values (186)201
34.5%
ValueCountFrequency (%)
0354
60.8%
21
 
0.2%
33
 
0.5%
42
 
0.3%
51
 
0.2%
61
 
0.2%
81
 
0.2%
111
 
0.2%
121
 
0.2%
132
 
0.3%
ValueCountFrequency (%)
2003221
0.2%
1863101
0.2%
1849811
0.2%
1828901
0.2%
1718881
0.2%
1678421
0.2%
1634061
0.2%
1500991
0.2%
1436531
0.2%
1371911
0.2%

banner
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct10
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.0412371
Minimum0
Maximum61000
Zeros569
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:36.317598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum61000
Range61000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2543.12093
Coefficient of variation (CV)21.18539421
Kurtosis568.1774826
Mean120.0412371
Median Absolute Deviation (MAD)0
Skewness23.71926611
Sum69864
Variance6467464.064
MonotonicityNot monotonic
2022-05-02T13:37:36.386074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0569
97.8%
24
 
0.7%
12
 
0.3%
281
 
0.2%
181
 
0.2%
501
 
0.2%
241
 
0.2%
25181
 
0.2%
610001
 
0.2%
62161
 
0.2%
ValueCountFrequency (%)
0569
97.8%
12
 
0.3%
24
 
0.7%
181
 
0.2%
241
 
0.2%
281
 
0.2%
501
 
0.2%
25181
 
0.2%
62161
 
0.2%
610001
 
0.2%
ValueCountFrequency (%)
610001
 
0.2%
62161
 
0.2%
25181
 
0.2%
501
 
0.2%
281
 
0.2%
241
 
0.2%
181
 
0.2%
24
 
0.7%
12
 
0.3%
0569
97.8%

sum
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct493
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278768.1271
Minimum1
Maximum4636206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-05-02T13:37:36.476075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q1729.75
median6576.5
Q378860.25
95-th percentile1913842.75
Maximum4636206
Range4636205
Interquartile range (IQR)78130.5

Descriptive statistics

Standard deviation673950.856
Coefficient of variation (CV)2.417603701
Kurtosis9.457623496
Mean278768.1271
Median Absolute Deviation (MAD)6573.5
Skewness2.991150483
Sum162243050
Variance4.542097563 × 1011
MonotonicityNot monotonic
2022-05-02T13:37:36.579074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220
 
3.4%
119
 
3.3%
310
 
1.7%
86
 
1.0%
20005
 
0.9%
64
 
0.7%
93
 
0.5%
43523
 
0.5%
2723
 
0.5%
103
 
0.5%
Other values (483)506
86.9%
ValueCountFrequency (%)
119
3.3%
220
3.4%
310
1.7%
43
 
0.5%
51
 
0.2%
64
 
0.7%
72
 
0.3%
86
 
1.0%
93
 
0.5%
103
 
0.5%
ValueCountFrequency (%)
46362061
0.2%
38656031
0.2%
37616091
0.2%
34683681
0.2%
32331931
0.2%
31344941
0.2%
29943621
0.2%
28910341
0.2%
28281421
0.2%
26384081
0.2%

Interactions

2022-05-02T13:37:32.740489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.173300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.095481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.037480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.073793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.958794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.999338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.879029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.744651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.823652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.828489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.265297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.187484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.130483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.163794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.053792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.091334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.966028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.839649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.920655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.917491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.360483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.281482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.224482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.255792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.147794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.179336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.058027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.940651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.014649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:33.003493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.450480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.373481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.321796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.342791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.246791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.270342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.141026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.037650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.105653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:33.089496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.535484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.463483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.525791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.428793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.338336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.363028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.226027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.128650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.199649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:33.188490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.626482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.576483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.620793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.521793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.434337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.457030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.316649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.230649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.290650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:33.269490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.717480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.665483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.714792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.605795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.522334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.540030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.400649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.432651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.378491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:33.353492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.809481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.750480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.798794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.689794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.608337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.619032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.481650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.527650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.465489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:33.449492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:24.915481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.850482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.894793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.784794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.707336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.711026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.572649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.631652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.562490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:33.532490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.011483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:25.950482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:26.983792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:27.873794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:28.799338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:29.796027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:30.662653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:31.725653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T13:37:32.653492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-02T13:37:36.667073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-02T13:37:36.792073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-02T13:37:36.915072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-02T13:37:37.026074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-02T13:37:37.128073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-02T13:37:33.838491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-02T13:37:34.028489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

categoryadvertiserproductdateitemTVradionewspapermagazinecablejongUCCbannersum
0성인종합영양제구주제약구주알코덱스19.10금액0124405000000124405
1성인종합영양제구주제약구주알코덱스19.10횟수0498000000498
2성인종합영양제구주제약구주알코덱스19.10노출량099600000009960
3성인종합영양제구주제약구주알코덱스19.11금액0113545000000113545
4성인종합영양제구주제약구주알코덱스19.11횟수0437000000437
5성인종합영양제구주제약구주알코덱스19.11노출량087400000008740
6성인종합영양제구주제약구주알코덱스19.12금액0111743000000111743
7성인종합영양제구주제약구주알코덱스19.12횟수0473000000473
8성인종합영양제구주제약구주알코덱스19.12노출량094600000009460
9성인종합영양제구주제약구주알코덱스20.01금액0109798000000109798

Last rows

categoryadvertiserproductdateitemTVradionewspapermagazinecablejongUCCbannersum
572성인종합영양제GC녹십자GC녹십자비맥스액티브정20.01금액000800000008000
573성인종합영양제GC녹십자GC녹십자비맥스액티브정20.01횟수000200002
574성인종합영양제GC녹십자GC녹십자비맥스액티브정20.01노출량000200002
575성인종합영양제GC녹십자GC녹십자비맥스액티브정20.02금액00010500000010500
576성인종합영양제GC녹십자GC녹십자비맥스액티브정20.02횟수000300003
577성인종합영양제GC녹십자GC녹십자비맥스액티브정20.02노출량000300003
578성인종합영양제GC녹십자GC녹십자비맥스액티브정20.03금액000400000004000
579성인종합영양제GC녹십자GC녹십자비맥스액티브정20.03횟수000200002
580성인종합영양제GC녹십자GC녹십자비맥스액티브정20.03노출량000200002
581성인종합영양제GC녹십자GC녹십자비맥스정21.02횟수000000011